Rebuilding an encoded data slice within a dispersed storage network

ABSTRACT

A method begins with a computing device of a dispersed storage network (DSN) determining that an encoded data slice of a set of encoded data slices requires rebuilding and sending partial rebuild requests to storage units of the DSN. The method continues with one of the storage units generating a partial rebuilt slice based one or more encoded data slices of the set of encoded data slices stored by the one of the storage units and securing the partial rebuilt slice using a shared secret scheme that is shared among the storage units to produce a secured partial rebuilt slice. The method continues with the computing device receiving a set of secured partial rebuilt slices from the storage units, recovering a set of partial rebuilt slices from the set of secured partial rebuilt slices, and rebuilding the encoded data slice from the set of partial rebuilt slices.

CROSS REFERENCE TO RELATED PATENTS

The present U.S. Utility Patent Application claims priority pursuant to35 U.S.C. §120, as a continuation, to the following U.S. Utility PatentApplication, which is hereby incorporated herein by reference in itsentirety and made part of the present U.S. Utility Patent Applicationfor all purposes:

-   1. U.S. Utility application Ser. No. 12/862,887, entitled “DISPERSED    STORAGE NETWORK DATA SLICE INTEGRITY VERIFICATION,” filed Aug. 25,    2010, pending, which claims priority pursuant to 35 U.S.C. §119(e)    to the following U.S. Provisional Patent Application:-   a. U.S. Provisional Application Ser. No. 61/264,072, entitled    “DISTRIBUTED STORAGE NETWORK REBUILDING,” filed Nov. 24, 2009.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not Applicable

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not Applicable

BACKGROUND OF THE INVENTION

1. Technical Field of the Invention

This invention relates generally to computing systems and moreparticularly to data storage solutions within such computing systems.

2. Description of Related Art

Computers are known to communicate, process, and store data. Suchcomputers range from wireless smart phones to data centers that supportmillions of web searches, stock trades, or on-line purchases every day.In general, a computing system generates data and/or manipulates datafrom one form into another. For instance, an image sensor of thecomputing system generates raw picture data and, using an imagecompression program (e.g., JPEG, MPEG, etc.), the computing systemmanipulates the raw picture data into a standardized compressed image.

With continued advances in processing speed and communication speed,computers are capable of processing real time multimedia data forapplications ranging from simple voice communications to streaming highdefinition video. As such, general-purpose information appliances arereplacing purpose-built communications devices (e.g., a telephone). Forexample, smart phones can support telephony communications but they arealso capable of text messaging and accessing the internet to performfunctions including email, web browsing, remote applications access, andmedia communications (e.g., telephony voice, image transfer, musicfiles, video files, real time video streaming. etc.).

Each type of computer is constructed and operates in accordance with oneor more communication, processing, and storage standards. As a result ofstandardization and with advances in technology, more and moreinformation content is being converted into digital formats. Forexample, more digital cameras are now being sold than film cameras, thusproducing more digital pictures. As another example, web-basedprogramming is becoming an alternative to over the air televisionbroadcasts and/or cable broadcasts. As further examples, papers, books,video entertainment, home video, etc. are now being stored digitally,which increases the demand on the storage function of computers.

A typical computer storage system includes one or more memory devicesaligned with the needs of the various operational aspects of thecomputer's processing and communication functions. Generally, theimmediacy of access dictates what type of memory device is used. Forexample, random access memory (RAM) memory can be accessed in any randomorder with a constant response time, thus it is typically used for cachememory and main memory. By contrast, memory device technologies thatrequire physical movement such as magnetic disks, tapes, and opticaldiscs, have a variable response time as the physical movement can takelonger than the data transfer, thus they are typically used forsecondary memory (e.g., hard drive, backup memory, etc.).

A computer's storage system will be compliant with one or more computerstorage standards that include, but are not limited to, network filesystem (NFS), flash file system (FFS), disk file system (DFS), smallcomputer system interface (SCSI), internet small computer systeminterface (iSCSI), file transfer protocol (FTP), and web-baseddistributed authoring and versioning (WebDAV). These standards specifythe data storage format (e.g., files, data objects, data blocks,directories, etc.) and interfacing between the computer's processingfunction and its storage system, which is a primary function of thecomputer's memory controller.

Despite the standardization of the computer and its storage system,memory devices fail; especially commercial grade memory devices thatutilize technologies incorporating physical movement (e.g., a discdrive). For example, it is fairly common for a disc drive to routinelysuffer from bit level corruption and to completely fail after threeyears of use. One solution is to utilize a higher-grade disc drive,which adds significant cost to a computer.

Another solution is to utilize multiple levels of redundant disc drivesto replicate the data into two or more copies. One such redundant driveapproach is called redundant array of independent discs (RAID). In aRAID device, a RAID controller adds parity data to the original databefore storing it across the array. The parity data is calculated fromthe original data such that the failure of a disc will not result in theloss of the original data. For example, RAID 5 uses three discs toprotect data from the failure of a single disc. The parity data, andassociated redundancy overhead data, reduces the storage capacity ofthree independent discs by one third (e.g., n−1=capacity). RAID 6 canrecover from a loss of two discs and requires a minimum of four discswith a storage capacity of n−2.

While RAID addresses the memory device failure issue, it is not withoutits own failure issues that affect its effectiveness, efficiency andsecurity. For instance, as more discs are added to the array, theprobability of a disc failure increases, which increases the demand formaintenance. For example, when a disc fails, it needs to be manuallyreplaced before another disc fails and the data stored in the RAIDdevice is lost. To reduce the risk of data loss, data on a RAID deviceis typically copied on to one or more other RAID devices. While thisaddresses the loss of data issue, it raises a security issue sincemultiple copies of data are available, which increases the chances ofunauthorized access. Further, as the amount of data being stored grows,the overhead of RAID devices becomes a non-trivial efficiency issue.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a computingsystem in accordance with the invention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the invention;

FIG. 3 is a schematic block diagram of an embodiment of a distributedstorage processing unit in accordance with the invention;

FIG. 4 is a schematic block diagram of an embodiment of a grid module inaccordance with the invention;

FIG. 5 is a diagram of an example embodiment of error coded data slicecreation in accordance with the invention;

FIG. 6 is a flowchart illustrating an example of modifying an errorcoding dispersal storage function parameter in accordance with theinvention;

FIG. 7 is a flowchart illustrating an example of generating integritychecking elements in accordance with the invention;

FIG. 8 is a flowchart illustrating an example of verifying encoded dataslice integrity in accordance with the invention;

FIG. 9 is a flowchart illustrating an example of rebuilding encoded dataslices in accordance with the invention;

FIG. 10 is a flowchart illustrating another example of rebuildingencoded data slices in accordance with the invention;

FIG. 11 is a schematic block diagram of another embodiment of acomputing system in accordance with the invention;

FIG. 12 is a schematic block diagram of an embodiment of a plurality ofgrid modules in accordance with the invention;

FIG. 13 is a schematic block diagram of another embodiment of a gridmodule in accordance with the invention;

FIG. 14 is a schematic block diagram of another embodiment of a gridmodule in accordance with the invention;

FIG. 15 is a schematic block diagram of another embodiment of a gridmodule in accordance with the invention;

FIG. 16 is a schematic block diagram of another embodiment of a gridmodule in accordance with the invention;

FIG. 17 is a flowchart illustrating an example of optimizing memoryusage in accordance with the invention;

FIG. 18 is a flowchart illustrating another example of optimizing memoryusage in accordance with the invention;

FIG. 19 is a flowchart illustrating another example of optimizing memoryusage in accordance with the invention; and

FIG. 20 is a flowchart illustrating another example of optimizing memoryusage in accordance with the invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of a computing system 10 thatincludes one or more of a first type of user devices 12, one or more ofa second type of user devices 14, at least one distributed storage (DS)processing unit 16, at least one DS managing unit 18, at least onestorage integrity processing unit 20, and a distributed storage network(DSN) memory 22 coupled via a network 24. The network 24 may include oneor more wireless and/or wire lined communication systems; one or moreprivate intranet systems and/or public internet systems; and/or one ormore local area networks (LAN) and/or wide area networks (WAN).

The DSN memory 22 includes a plurality of distributed storage (DS) units36 for storing data of the system. Each of the DS units 36 includes aprocessing module and memory and may be located at a geographicallydifferent site than the other DS units (e.g., one in Chicago, one inMilwaukee, etc.). The processing module may be a single processingdevice or a plurality of processing devices. Such a processing devicemay be a microprocessor, micro-controller, digital signal processor,microcomputer, central processing unit, field programmable gate array,programmable logic device, state machine, logic circuitry, analogcircuitry, digital circuitry, and/or any device that manipulates signals(analog and/or digital) based on hard coding of the circuitry and/oroperational instructions. The processing module may have an associatedmemory and/or memory element, which may be a single memory device, aplurality of memory devices, and/or embedded circuitry of the processingmodule. Such a memory device may be a read-only memory, random accessmemory, volatile memory, non-volatile memory, static memory, dynamicmemory, flash memory, cache memory, and/or any device that storesdigital information. Note that if the processing module includes morethan one processing device, the processing devices may be centrallylocated (e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that when the processing module implements one or more ofits functions via a state machine, analog circuitry, digital circuitry,and/or logic circuitry, the memory and/or memory element storing thecorresponding operational instructions may be embedded within, orexternal to, the circuitry comprising the state machine, analogcircuitry, digital circuitry, and/or logic circuitry. Still further notethat, the memory element stores, and the processing module executes,hard coded and/or operational instructions corresponding to at leastsome of the steps and/or functions illustrated in FIGS. 1-20.

Each of the user devices 12-14, the DS processing unit 16, the DSmanaging unit 18, and the storage integrity processing unit 20 may be aportable computing device (e.g., a social networking device, a gamingdevice, a cell phone, a smart phone, a personal digital assistant, adigital music player, a digital video player, a laptop computer, ahandheld computer, a video game controller, and/or any other portabledevice that includes a computing core) and/or a fixed computing device(e.g., a personal computer, a computer server, a cable set-top box, asatellite receiver, a television set, a printer, a fax machine, homeentertainment equipment, a video game console, and/or any type of homeor office computing equipment). Such a portable or fixed computingdevice includes a computing core 26 and one or more interfaces 30, 32,and/or 33. An embodiment of the computing core 26 will be described withreference to FIG. 2.

With respect to the interfaces, each of the interfaces 30, 32, and 33includes software and/or hardware to support one or more communicationlinks via the network 24 and/or directly. For example, interface 30supports a communication link (wired, wireless, direct, via a LAN, viathe network 24, etc.) between the second type of user device 14 and theDS processing unit 16. As another example, DSN interface 32 supports aplurality of communication links via the network 24 between the DSNmemory 22 and the DS processing unit 16, the first type of user device12, and/or the storage integrity processing unit 20. As yet anotherexample, interface 33 supports a communication link between the DSmanaging unit 18 and any one of the other devices and/or units 12, 14,16, 20, and/or 22 via the network 24.

In general and with respect to data storage, the system 10 supportsthree primary functions: distributed network data storage management,distributed data storage and retrieval, and data storage integrityverification. In accordance with these three primary functions, data canbe distributedly stored in a plurality of physically different locationsand subsequently retrieved in a reliable and secure manner regardless offailures of individual storage devices, failures of network equipment,the duration of storage, the amount of data being stored, attempts athacking the data, etc.

The DS managing unit 18 performs distributed network data storagemanagement functions, which include establishing distributed datastorage parameters, performing network operations, performing networkadministration, and/or performing network maintenance. The DS managingunit 18 establishes the distributed data storage parameters (e.g.,allocation of virtual DSN memory space, distributed storage parameters,security parameters, billing information, user profile information,etc.) for one or more of the user devices 12-14 (e.g., established forindividual devices, established for a user group of devices, establishedfor public access by the user devices, etc.). For example, the DSmanaging unit 18 coordinates the creation of a vault (e.g., a virtualmemory block) within the DSN memory 22 for a user device (for a group ofdevices, or for public access). The DS managing unit 18 also determinesthe distributed data storage parameters for the vault. In particular,the DS managing unit 18 determines a number of slices (e.g., the numberthat a data segment of a data file and/or data block is partitioned intofor distributed storage) and a read threshold value (e.g., the minimumnumber of slices required to reconstruct the data segment).

As another example, the DS managing unit 18 creates and stores, locallyor within the DSN memory 22, user profile information. The user profileinformation includes one or more of authentication information,permissions, and/or the security parameters. The security parameters mayinclude one or more of encryption/decryption scheme, one or moreencryption keys, key generation scheme, and data encoding/decodingscheme.

As yet another example, the DS managing unit 18 creates billinginformation for a particular user, user group, vault access, publicvault access, etc. For instance, the DS managing unit 18 tracks thenumber of times a user accesses a private vault and/or public vaults,which can be used to generate a per-access bill. In another instance,the DS managing unit 18 tracks the amount of data stored and/orretrieved by a user device and/or a user group, which can be used togenerate a per-data-amount bill.

The DS managing unit 18 also performs network operations, networkadministration, and/or network maintenance. As at least part ofperforming the network operations and/or administration, the DS managingunit 18 monitors performance of the devices and/or units of the system10 for potential failures, determines the devices' and/or units'activation status, determines the devices' and/or units' loading, andany other system level operation that affects the performance level ofthe system 10. For example, the DS managing unit 18 receives andaggregates network management alarms, alerts, errors, statusinformation, performance information, and messages from the devices12-14 and/or the units 16, 20, 22. For example, the DS managing unit 18receives a simple network management protocol (SNMP) message regardingthe status of the DS processing unit 16.

The DS managing unit 18 performs the network maintenance by identifyingequipment within the system 10 that needs replacing, upgrading,repairing, and/or expanding. For example, the DS managing unit 18determines that the DSN memory 22 needs more DS units 36 or that one ormore of the DS units 36 needs updating.

The second primary function (i.e., distributed data storage andretrieval) begins and ends with a user device 12-14. For instance, if asecond type of user device 14 has a data file 38 and/or data block 40 tostore in the DSN memory 22, it sends the data file 38 and/or data block40 to the DS processing unit 16 via its interface 30. As will bedescribed in greater detail with reference to FIG. 2, the interface 30functions to mimic a conventional operating system (OS) file systeminterface (e.g., network file system (NFS), flash file system (FFS),disk file system (DFS), file transfer protocol (FTP), web-baseddistributed authoring and versioning (WebDAV), etc.) and/or a blockmemory interface (e.g., small computer system interface (SCSI), internetsmall computer system interface (iSCSI), etc.). In addition, theinterface 30 may attach a user identification code (ID) to the data file38 and/or data block 40.

The DS processing unit 16 receives the data file 38 and/or data block 40via its interface 30 and performs a distributed storage (DS) process 34thereon (e.g., an error coding dispersal storage function). The DSprocessing 34 begins by partitioning the data file 38 and/or data block40 into one or more data segments, which is represented as Y datasegments. For example, the DS processing 34 may partition the data file38 and/or data block 40 into a fixed byte size segment (e.g., 2¹ to2^(n) bytes, where n=>2) or a variable byte size (e.g., change byte sizefrom segment to segment, or from groups of segments to groups ofsegments, etc.).

For each of the Y data segments, the DS processing 34 error encodes(e.g., forward error correction (FEC), information dispersal algorithm,or error correction coding) and slices (or slices then error encodes)the data segment into a plurality of error coded (EC) data slices 42-48,which is represented as X slices per data segment. The number of slices(X) per segment, which corresponds to a number of pillars n, is set inaccordance with the distributed data storage parameters and the errorcoding scheme. For example, if a Reed-Solomon (or other FEC scheme) isused in an n/k system, then a data segment is divided into n slices,where k number of slices is needed to reconstruct the original data(i.e., k is the threshold). As a few specific examples, the n/k factormay be 5/3; 6/4; 8/6; 8/5; 16/10.

For each EC slice 42-48, the DS processing unit 16 creates a uniqueslice name and appends it to the corresponding EC slice 42-48. The slicename includes universal DSN memory addressing routing information (e.g.,virtual memory addresses in the DSN memory 22) and user-specificinformation (e.g., user ID, file name, data block identifier, etc.).

The DS processing unit 16 transmits the plurality of EC slices 42-48 toa plurality of DS units 36 of the DSN memory 22 via the DSN interface 32and the network 24. The DSN interface 32 formats each of the slices fortransmission via the network 24. For example, the DSN interface 32 mayutilize an internet protocol (e.g., TCP/IP, etc.) to packetize the ECslices 42-48 for transmission via the network 24.

The number of DS units 36 receiving the EC slices 42-48 is dependent onthe distributed data storage parameters established by the DS managingunit 18. For example, the DS managing unit 18 may indicate that eachslice is to be stored in a different DS unit 36. As another example, theDS managing unit 18 may indicate that like slice numbers of differentdata segments are to be stored in the same DS unit 36. For example, thefirst slice of each of the data segments is to be stored in a first DSunit 36, the second slice of each of the data segments is to be storedin a second DS unit 36, etc. In this manner, the data is encoded anddistributedly stored at physically diverse locations to improve datastorage integrity and security. Further examples of encoding the datasegments will be provided with reference to one or more of FIGS. 2-20.

Each DS unit 36 that receives an EC slice 42-48 for storage translatesthe virtual DSN memory address of the slice into a local physicaladdress for storage. Accordingly, each DS unit 36 maintains a virtual tophysical memory mapping to assist in the storage and retrieval of data.

The first type of user device 12 performs a similar function to storedata in the DSN memory 22 with the exception that it includes the DSprocessing. As such, the device 12 encodes and slices the data fileand/or data block it has to store. The device then transmits the slices11 to the DSN memory via its DSN interface 32 and the network 24.

For a second type of user device 14 to retrieve a data file or datablock from memory, it issues a read command via its interface 30 to theDS processing unit 16. The DS processing unit 16 performs the DSprocessing 34 to identify the DS units 36 storing the slices of the datafile and/or data block based on the read command. The DS processing unit16 may also communicate with the DS managing unit 18 to verify that theuser device 14 is authorized to access the requested data.

Assuming that the user device is authorized to access the requesteddata, the DS processing unit 16 issues slice read commands to at least athreshold number of the DS units 36 storing the requested data (e.g., toat least 10 DS units for a 16/10 error coding scheme). Each of the DSunits 36 receiving the slice read command, verifies the command,accesses its virtual to physical memory mapping, retrieves the requestedslice, or slices, and transmits it to the DS processing unit 16.

Once the DS processing unit 16 has received a read threshold number ofslices for a data segment, it performs an error decoding function andde-slicing to reconstruct the data segment. When Y number of datasegments has been reconstructed, the DS processing unit 16 provides thedata file 38 and/or data block 40 to the user device 14. Note that thefirst type of user device 12 performs a similar process to retrieve adata file and/or data block.

The storage integrity processing unit 20 performs the third primaryfunction of data storage integrity verification. In general, the storageintegrity processing unit 20 periodically retrieves slices 45, and/orslice names, of a data file or data block of a user device to verifythat one or more slices have not been corrupted or lost (e.g., the DSunit failed). The retrieval process mimics the read process previouslydescribed.

If the storage integrity processing unit 20 determines that one or moreslices is corrupted or lost, it rebuilds the corrupted or lost slice(s)in accordance with the error coding scheme. The storage integrityprocessing unit 20 stores the rebuilt slice, or slices, in theappropriate DS unit(s) 36 in a manner that mimics the write processpreviously described.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface 60, at least one IO device interface module 62, a readonly memory (ROM) basic input output system (BIOS) 64, and one or morememory interface modules. The memory interface module(s) includes one ormore of a universal serial bus (USB) interface module 66, a host busadapter (HBA) interface module 68, a network interface module 70, aflash interface module 72, a hard drive interface module 74, and a DSNinterface module 76. Note the DSN interface module 76 and/or the networkinterface module 70 may function as the interface 30 of the user device14 of FIG. 1. Further note that the IO device interface module 62 and/orthe memory interface modules may be collectively or individuallyreferred to as IO ports.

The processing module 50 may be a single processing device or aplurality of processing devices. Such a processing device may be amicroprocessor, micro-controller, digital signal processor,microcomputer, central processing unit, field programmable gate array,programmable logic device, state machine, logic circuitry, analogcircuitry, digital circuitry, and/or any device that manipulates signals(analog and/or digital) based on hard coding of the circuitry and/oroperational instructions. The processing module 50 may have anassociated memory and/or memory element, which may be a single memorydevice, a plurality of memory devices, and/or embedded circuitry of theprocessing module 50. Such a memory device may be a read-only memory,random access memory, volatile memory, non-volatile memory, staticmemory, dynamic memory, flash memory, cache memory, and/or any devicethat stores digital information. Note that if the processing module 50includes more than one processing device, the processing devices may becentrally located (e.g., directly coupled together via a wired and/orwireless bus structure) or may be distributedly located (e.g., cloudcomputing via indirect coupling via a local area network and/or a widearea network). Further note that when the processing module 50implements one or more of its functions via a state machine, analogcircuitry, digital circuitry, and/or logic circuitry, the memory and/ormemory element storing the corresponding operational instructions may beembedded within, or external to, the circuitry comprising the statemachine, analog circuitry, digital circuitry, and/or logic circuitry.Still further note that, the memory element stores, and the processingmodule 50 executes, hard coded and/or operational instructionscorresponding to at least some of the steps and/or functions illustratedin FIGS. 1-20.

FIG. 3 is a schematic block diagram of an embodiment of a dispersedstorage (DS) processing module 34 of user device 12 and/or of the DSprocessing unit 16. The DS processing module 34 includes a gatewaymodule 78, an access module 80, a grid module 82, and a storage module84. The DS processing module 34 may also include an interface 30 and theDSnet interface 32 or the interfaces 68 and/or 70 may be part of userdevice 12 or of the DS processing unit 16. The DS processing module 34may further include a bypass/feedback path between the storage module 84to the gateway module 78. Note that the modules 78-84 of the DSprocessing module 34 may be in a single unit or distributed acrossmultiple units.

In an example of storing data, the gateway module 78 receives anincoming data object that includes a user ID field 86, an object namefield 88, and the data object field 40 and may also receivecorresponding information that includes a process identifier (e.g., aninternal process/application ID), metadata, a file system directory, ablock number, a transaction message, a user device identity (ID), a dataobject identifier, a source name, and/or user information. The gatewaymodule 78 authenticates the user associated with the data object byverifying the user ID 86 with the DS managing unit 18 and/or anotherauthenticating unit.

When the user is authenticated, the gateway module 78 obtains userinformation from the management unit 18, the user device, and/or theother authenticating unit. The user information includes a vaultidentifier, operational parameters, and user attributes (e.g., userdata, billing information, etc.). A vault identifier identifies a vault,which is a virtual memory space that maps to a set of DS storage units36. For example, vault 1 (i.e., user 1's DSN memory space) includeseight DS storage units (X=8 wide) and vault 2 (i.e., user 2's DSN memoryspace) includes sixteen DS storage units (X=16 wide). The operationalparameters may include an error coding algorithm, the width n (number ofpillars X or slices per segment for this vault), a read threshold T, awrite threshold, an encryption algorithm, a slicing parameter, acompression algorithm, an integrity check method, caching settings,parallelism settings, and/or other parameters that may be used to accessthe DSN memory layer.

The gateway module 78 uses the user information to assign a source name35 to the data. For instance, the gateway module 78 determines thesource name 35 of the data object 40 based on the vault identifier andthe data object. For example, the source name 35 may contain a fileidentifier (ID), a vault generation number, a reserved field, and avault identifier (ID). As another example, the gateway module 78 maygenerate the file ID based on a hash function of the data object 40.Note that the gateway module 78 may also perform message conversion,protocol conversion, electrical conversion, optical conversion, accesscontrol, user identification, user information retrieval, trafficmonitoring, statistics generation, configuration, management, and/orsource name determination.

The access module 80 receives the data object 40 and creates a series ofdata segments 1 through Y 90-92 in accordance with a data storageprotocol (e.g., file storage system, a block storage system, and/or anaggregated block storage system). The number of segments Y may be chosenor randomly assigned based on a selected segment size and the size ofthe data object. For example, if the number of segments is chosen to bea fixed number, then the size of the segments varies as a function ofthe size of the data object. For instance, if the data object is animage file of 4,194,304 eight bit bytes (e.g., 33,554,432 bits) and thenumber of segments Y=131,072, then each segment is 256 bits or 32 bytes.As another example, if segment size is fixed, then the number ofsegments Y varies based on the size of data object. For instance, if thedata object is an image file of 4,194,304 bytes and the fixed size ofeach segment is 4,096 bytes, then the then number of segments Y=1,024.Note that each segment is associated with the same source name.

The grid module 82 receives the data segments and may manipulate (e.g.,compression, encryption, cyclic redundancy check (CRC), etc.) each ofthe data segments before performing an error coding function of theerror coding dispersal storage function to produce a pre-manipulateddata segment. After manipulating a data segment, if applicable, the gridmodule 82 error encodes (e.g., Reed-Solomon, Convolution encoding,Trellis encoding, etc.) the data segment or manipulated data segmentinto X error coded data slices 42-44.

The value X, or the number of pillars (e.g., X=16), is chosen as aparameter of the error coding dispersal storage function. Otherparameters of the error coding dispersal function include a readthreshold T, a write threshold W, etc. The read threshold (e.g., T=10,when X=16) corresponds to the minimum number of error-free error codeddata slices required to reconstruct the data segment. In other words,the DS processing module 34 can compensate for X-T (e.g., 16-10=6)missing error coded data slices per data segment. The write threshold Wcorresponds to a minimum number of DS storage units that acknowledgeproper storage of their respective data slices before the DS processingmodule indicates proper storage of the encoded data segment. Note thatthe write threshold is greater than or equal to the read threshold for agiven number of pillars (X).

For each data slice of a data segment, the grid module 82 generates aunique slice name 37 and attaches it thereto. The slice name 37 includesa universal routing information field and a vault specific field and maybe 48 bytes (e.g., 24 bytes for each of the universal routinginformation field and the vault specific field). As illustrated, theuniversal routing information field includes a slice index, a vault ID,a vault generation, and a reserved field. The slice index is based onthe pillar number and the vault ID and, as such, is unique for eachpillar (e.g., slices of the same pillar for the same vault for anysegment will share the same slice index). The vault specific fieldincludes a data name, which includes a file ID and a segment number(e.g., a sequential numbering of data segments 1-Y of a simple dataobject or a data block number).

Prior to outputting the error coded data slices of a data segment, thegrid module may perform post-slice manipulation on the slices. Ifenabled, the manipulation includes slice level compression, encryption,CRC, addressing, tagging, and/or other manipulation to improve theeffectiveness of the computing system.

When the error coded data slices of a data segment are ready to beoutputted, the grid module 82 determines which of the DS storage units36 will store the EC data slices based on a dispersed storage memorymapping associated with the user's vault and/or DS storage unitattributes. The DS storage unit attributes may include availability,self-selection, performance history, link speed, link latency,ownership, available DSN memory, domain, cost, a prioritization scheme,a centralized selection message from another source, a lookup table,data ownership, and/or any other factor to optimize the operation of thecomputing system. Note that the number of DS storage units 36 is equalto or greater than the number of pillars (e.g., X) so that no more thanone error coded data slice of the same data segment is stored on thesame DS storage unit 36. Further note that EC data slices of the samepillar number but of different segments (e.g., EC data slice 1 of datasegment 1 and EC data slice 1 of data segment 2) may be stored on thesame or different DS storage units 36.

The storage module 84 performs an integrity check on the outboundencoded data slices and, when successful, identifies a plurality of DSstorage units based on information provided by the grid module 82. Thestorage module 84 then outputs the encoded data slices 1 through X ofeach segment 1 through Y to the DS storage units 36. Each of the DSstorage units 36 stores its EC data slice(s) and maintains a localvirtual DSN address to physical location table to convert the virtualDSN address of the EC data slice(s) into physical storage addresses.

In an example of a read operation, the user device 12 and/or 14 sends aread request to the DS processing unit 16, which authenticates therequest. When the request is authentic, the DS processing unit 16 sendsa read message to each of the DS storage units 36 storing slices of thedata object being read. The slices are received via the DSnet interface32 and processed by the storage module 84, which performs a parity checkand provides the slices to the grid module 82 when the parity check wassuccessful. The grid module 82 decodes the slices in accordance with theerror coding dispersal storage function to reconstruct the data segment.The access module 80 reconstructs the data object from the data segmentsand the gateway module 78 formats the data object for transmission tothe user device.

FIG. 4 is a schematic block diagram of an embodiment of a grid module 82that includes a control unit 73, a pre-slice manipulator 75, an encoder77, a slicer 79, a post-slice manipulator 81, a pre-slice de-manipulator83, a decoder 85, a de-slicer 87, and/or a post-slice de-manipulator 89.Note that the control unit 73 may be partially or completely external tothe grid module 82. For example, the control unit 73 may be part of thecomputing core at a remote location, part of a user device, part of theDS managing unit 18, or distributed amongst one or more DS storageunits.

In an example of a write operation, the pre-slice manipulator 75receives a data segment 90-92 and a write instruction from an authorizeduser device. The pre-slice manipulator 75 determines if pre-manipulationof the data segment 90-92 is required and, if so, what type. Thepre-slice manipulator 75 may make the determination independently orbased on instructions from the control unit 73, where the determinationis based on a computing system-wide predetermination, a table lookup,vault parameters associated with the user identification, the type ofdata, security requirements, available DSN memory, performancerequirements, and/or other metadata.

Once a positive determination is made, the pre-slice manipulator 75manipulates the data segment 90-92 in accordance with the type ofmanipulation. For example, the type of manipulation may be compression(e.g., Lempel-Ziv-Welch, Huffman, Golomb, fractal, wavelet, etc.),signatures (e.g., Digital Signature Algorithm (DSA), Elliptic Curve DSA,Secure Hash Algorithm, etc.), watermarking, tagging, encryption (e.g.,Data Encryption Standard, Advanced Encryption Standard, etc.), addingmetadata (e.g., time/date stamping, user information, file type, etc.),cyclic redundancy check (e.g., CRC32), and/or other data manipulationsto produce the pre-manipulated data segment.

The encoder 77 encodes the pre-manipulated data segment 92 using aforward error correction (FEC) encoder (and/or other type of erasurecoding and/or error coding) to produce an encoded data segment 94. Theencoder 77 determines which forward error correction algorithm to usebased on a predetermination associated with the user's vault, a timebased algorithm, user direction, DS managing unit direction, controlunit direction, as a function of the data type, as a function of thedata segment 92 metadata, and/or any other factor to determine algorithmtype. The forward error correction algorithm may be Golay,Multidimensional parity, Reed-Solomon, Hamming, Bose Ray ChauduriHocquenghem (BCH), Cauchy-Reed-Solomon, or any other FEC encoder. Notethat the encoder 77 may use a different encoding algorithm for each datasegment 92, the same encoding algorithm for the data segments 92 of adata object, or a combination thereof.

The encoded data segment 94 is of greater size than the data segment 92by the overhead rate of the encoding algorithm by a factor of X/T, whereX is the width or number of slices, and T is the read threshold. In thisregard, the corresponding decoding process can accommodate at most X-Tmissing EC data slices and still recreate the data segment 92. Forexample, if X=16 and T=10, then the data segment 92 will be recoverableas long as 10 or more EC data slices per segment are not corrupted.

The slicer 79 transforms the encoded data segment 94 into EC data slicesin accordance with the slicing parameter from the vault for this userand/or data segment 92. For example, if the slicing parameter is X=16,then the slicer 79 slices each encoded data segment 94 into 16 encodedslices.

The post-slice manipulator 81 performs, if enabled, post-manipulation onthe encoded slices to produce the EC data slices. If enabled, thepost-slice manipulator 81 determines the type of post-manipulation,which may be based on a computing system-wide predetermination,parameters in the vault for this user, a table lookup, the useridentification, the type of data, security requirements, available DSNmemory, performance requirements, control unit directed, and/or othermetadata. Note that the type of post-slice manipulation may includeslice level compression, signatures, encryption, CRC, addressing,watermarking, tagging, adding metadata, and/or other manipulation toimprove the effectiveness of the computing system.

In an example of a read operation, the post-slice de-manipulator 89receives at least a read threshold number of EC data slices and performsthe inverse function of the post-slice manipulator 81 to produce aplurality of encoded slices. The de-slicer 87 de-slices the encodedslices to produce an encoded data segment 94. The decoder 85 performsthe inverse function of the encoder 77 to recapture the data segment90-92. The pre-slice de-manipulator 83 performs the inverse function ofthe pre-slice manipulator 75 to recapture the data segment 90-92.

FIG. 5 is a diagram of an example of slicing an encoded data segment 94by the slicer 79. In this example, the encoded data segment 94 includesthirty-two bits, but may include more or less bits. The slicer 79disperses the bits of the encoded data segment 94 across the EC dataslices in a pattern as shown. As such, each EC data slice does notinclude consecutive bits of the data segment 94 reducing the impact ofconsecutive bit failures on data recovery. For example, if EC data slice2 (which includes bits 1, 5, 9, 13, 17, 25, and 29) is unavailable(e.g., lost, inaccessible, or corrupted), the data segment can bereconstructed from the other EC data slices (e.g., 1, 3 and 4 for a readthreshold of 3 and a width of 4).

FIG. 6 is a flowchart illustrating an example of modifying an errorcoding dispersal storage function parameter (e.g., an operationalparameter). The method begins at step 102 where a DS processingdetermines dispersed storage network (DSN) memory errors. Such errorsmay include one or more of missing data slices, data slices with errors,corrupted data slices, tampered data slices, an offline DS unit, anetwork failure, and a DS unit memory failure (e.g., a failed diskdrive). Such a determination may be based on one or more of a scan ofslice names present in a DS unit, a memory test, a comparison ofcalculated slice checksums to stored checksums, an integrity test, anetwork element ping test, and a command.

The method continues with step 104 where the DS processing corrects theDSN memory errors. For example, the DS processing retrieves at least aread threshold number of data slices for a data segment corresponding tothe data slice with the error, de-slicing the data slices, and decodesthe data slices in accordance with the error coding dispersal storagefunction parameters to produce the data segment. Next, the DS processingencodes and slices the data segment in accordance with the error codingdispersal storage function parameters to produce a set of encoded dataslices. The DS processing sends at least some data slices of the set ofencoded data slices with a store command to the DSN memory for storagetherein (e.g., the data slices are confirmed as stored in at least awrite threshold number of DS units). Alternatively, or in addition to,the DS processing determines new error coding dispersal storage functionparameters and encodes and slices the data segment in accordance withthe new error coding dispersal storage function parameters to producethe set of encoded data slices. Next, the DS processing stores at leastsome data slices of the set of encoded data slices with a store commandto the DSN memory for storage therein. Note that the DS processing maydetermine the new error coding dispersal storage function parametersbased in part on reliability information as will be discussed in greaterdetail below.

At step 106, the DS processing unit determines mean time to failure(MTTF) and mean time to repair (MTTR) information where MTTF measuresthe time between detected DSN memory errors for the same memory and MTTRmeasures the time between detecting the DSN memory error and correctingthe DSN memory error (e.g., the rebuilding time). Note that the MTTR maybe longer when larger disk drives are utilized as the memory since itmay take longer to read more data from the other pillars and then writemore recreated slices to the memory. The DS processing calculates theMTTF and MTTR information by retrieving MTTF and MTTR history fromstorage (e.g., the history records are stored in one or more of thestorage integrity processing unit and the DSN memory) and averaging theretrieved information with the current error detection scenario data. Atstep 108, The DS processing updates the MTTF and MTTR history by storingthe MTTF and MTTR information.

The method continues at step 110 where the DS processing determineswhether the MTTF compares favorably to a MTTF threshold and whether theMTTR compares favorably to a MTTR threshold. In an instance, the MTTFthreshold and MTTR threshold are associated with one or more of a user,a group of users, a vault, a group of vaults, a DS unit, a group of DSunits, and the whole computing system. The DS processing determines theMTTF threshold and MTTR threshold based on one or more of a vaultlookup, a system memory lookup, a group of vaults lookup, and a command.In an example, the DS processing determines that the MTTF comparesfavorably to a MTTF threshold when the MTTF is greater than the MTTFthreshold. For instance, the comparison is favorable when the MTTF is10,000 hours and the MTTF threshold is 9,000 hours. In another example,the DS processing determines that the MTTR compares favorably to a MTTRthreshold when the MTTR is less than the MTTR threshold. In an instance,the comparison is favorable when the MTTR is 1 hour and the MTTRthreshold is 3 hours.

The method branches back to step 102 when the DS processing determinesthat the MTTF compares favorably to the MTTF threshold and the MTTRcompares favorably to the MTTR threshold. The method continues to step112 when the DS processing determines that either the MTTF does notcompare favorably to the MTTF threshold or the MTTR does not comparefavorably to the MTTR threshold. In an example, either the MTTF is lessthan the MTTF threshold or the MTTR is greater than the MTTR threshold.In an instance, failures are happening too often and when they do therebuilding is taking too long.

At step 112, the DS processing retrieves slices from the affectedvault(s) of the DSN memory error to recreate the data segments and dataobjects in part by retrieving, de-slicing, and decoding in accordancewith the current error coding dispersal storage function parameters. Atstep 114, DS processing determines new error coding dispersal storagefunction parameters for the vault, which may include changing theparameters to improve the reliability and/or reduce the rebuild time.The new parameters may include the pillar width n, the read threshold,the write threshold, the encoding algorithm the slicing method etc. Sucha determination may be based on one or more of the current parameters,the MTTF, the MTTR, the comparison of the MTTF to the MTTF threshold,the comparison of the MTTR to the MTTR threshold, an error message, alookup, a predetermination, and a command. For example, the DSprocessing may change from a 16/10 system to a 32/20 system to improvereliability (e.g., pillar width 32/read threshold 20). At step 116, theDS processing creates new data slices of the data segments and dataobjects in accordance with the new error coding dispersal storagefunction parameters. The DS processing sends the new data slices to theDSN memory with a store command for storage in the DS units.

FIG. 7 is a flowchart illustrating an example of generating integritychecking elements. The method begins at step 118 where a processingmodule encodes a data segment in accordance with an error codingdispersal storage function to produce a set of encoded data slices. Forexample, the processing module encodes a data segment of a data objectfor storage. At step 120, the processing module determines a messageauthentication key wherein the message authentication key comprises atleast one of an output of a random number generator, a cryptographickey, an integrity check of the cryptographic key, a hash function of thecryptographic key, a result of a table lookup, and a result of aretrieval. For example, the processing module determines the messageauthentication key by utilizing the output of the random numbergenerator that is compatible with a key length of the messageauthentication key.

In another example, the processing module determines the messageauthentication key by combining the output of the random numbergenerator with a hash of the output of the random number generator. Notethat in this example, the hash may be subsequently utilized to determinethe validity of the random number portion (e.g., a cryptographic key) ofthe message authentication key. The processing module may generate afirst message authentication key for the data segment and generate asecond message authentication key for a second data segment. Forinstance, the first message authentication key is substantially the sameas the second message authentication key. In another instance, the firstmessage authentication key is substantially not the same as the secondmessage authentication key.

The method continues at step 122 where the processing module generatesan authentication code based on the message authentication key and anencoded data slice of the set of encoded data slices. For example, theprocessing module generates the authentication code by one of performinga keyed-hash message authentication code (HMAC) generation function onthe encoded data slice utilizing the message authentication key or byperforming a cryptographic hash function algorithm on the encoded dataslice utilizing the message authentication key. Examples of HMACalgorithms include a 16 byte HMAC-MD5 (e.g., message digest algorithm 5)and a 20 byte HMAC-SHA1 (e.g., secure hash algorithm). In addition, theprocessing module may generate a second authentication code based on themessage authentication key and a second encoded data slice of the set ofencoded data slices. For instance, the processing module may generate aset of authentication codes based on the message authentication key andeach of the set of encoded data slices. Note that the authenticationcode may be used to facilitate verification of the integrity and/orauthenticity of an encoded data slice.

The method continues at step 124 where the processing module encodes themessage authentication key into a set of secret shares based on at leastsome of the set of DS units (e.g., a pillar number). In an example, theprocessing module assigns the message authentication key to a constantof a polynomial. The polynomial may include multiple constants andmultiple variables. In an instance, the processing module assigns themessage authentication key to a constant m when the polynomial is of aform of y=m×+b. The processing module assigns a unique identifier (e.g.,the pillar number) of the corresponding one of the at least some of theset of DS units to a variable of the polynomial to produce a firstassigned variable. In an instance, the processing module assigns theunique identifier to a variable x when the polynomial is of the formy=m×+b. In addition, the processing module may determine values for oneor more other constants of the polynomial. Such a determination may bebased on one or more of a lookup, a request, a message, the messageauthentication key, and a command.

The processing module solves the polynomial to produce a secret sharebased on the constant, the first assigned variable, and the one or moreother constants. For instance, the processing module produces the secretshare in accordance with the polynomial y=m×+b=80*2+15=175, when themessage authentication key=m=80, the unique identifier=x=2 for pillar 2,and the other constant=b=15. In addition, the processing module mayassign a second unique identifier of a second one of the at least someof the set of DS units to the variable of the polynomial to produce asecond assigned variable followed by the processing module solving thepolynomial to produce a second secret share of the set of secret sharesbased on the constant and the second assigned variable. For instance,the processing module produces the second secret share in accordancewith the polynomial y=m×+b=80*3+15=255, when the message authenticationkey=m=80, the unique identifier=x=3 for pillar 3, and the otherconstant=b=15.

Alternatively, at step 124 the processing module encodes the messageauthentication key in accordance with an error coding dispersal storagefunction into the set of secret shares. For instance, the processingmodule encodes the message authentication key to produce an encodedmessage authentication key. Next, the processing module slices theencoded message authentication key to produce the set of secret shares.

At step 126, the processing module appends the authentication codeassociated with the encoded data slice to the encoded data slice. Inaddition, the processing module may append an authentication codeassociated with other encoded data slices of the set of encoded dataslices. Further, the processing module appends the set of secret sharesto associated encoded data slices of the set of encoded data slices. Forexample, the processing module appends a first secret share to a firstencoded data slice and appends a second secret share to a second encodeddata slice.

The method continues at step 128 where the processing module outputs theauthentication code and the encoded data slice to a dispersed storage(DS) unit of a set of DS units for storage therein. In addition, theprocessing module may output the second authentication code and thesecond encoded data slice to a second DS unit of a set of DS units whenthere is more than one authentication code. The processing moduleoutputs a secret share of the set of secret shares to a correspondingone of the at least some of the set of DS units for storage therein. Inaddition, the processing module may output the rest of the secret sharesof the set of secret shares to corresponding DS units of the set of DSunits for storage therein.

FIG. 8 is a flowchart illustrating an example of verifying encoded dataslice integrity. The method begins at step 130 where a processing moduleissues a retrieval request to retrieve one or more encoded data slices,one or more authentication codes, and one or more secret shares from oneor more DS units of a set of DS units. In an example, the processingmodule issues the retrieval request in response to receiving a dataobject retrieval request. In another example, the processing moduleissues that retrieval request in response to determining an encoded dataslice error. The processing module receives secret shares of a set ofsecret shares to produce received secret shares in response to theretrieval request. The processing module receives encoded data slices ofa set of encoded data slices to produce received encoded data slices inresponse to the retrieval request.

The method continues at step 132 where the processing module decodes thereceived secret shares in accordance with a secret share function torecapture a message authentication key when a threshold number of thesecret shares is received. In an example, the processing module performsthe secret share function by assigning a threshold number of uniqueidentifiers (e.g. pillar numbers) of the threshold number of receivedsecret shares to a first variable of a polynomial to produce an assignedvalue set of the first variable. For instance, the processing moduleassigns the unique identifiers to the variable x when the polynomial isof a form of y=m×+b (e.g., x=1 for pillar 1, x=2 for pillar 2, etc).Next, the processing module assigns the threshold number of receivedsecret shares to a second variable of the polynomial to produce anassigned value set of the second variable. As a more specific example,the processing module assigns the threshold number of received secretshares to the variable y when the polynomial is of the form of y=m×+b(e.g., y=175 for pillar 2, y=255 for pillar 3, etc.). In addition, theprocessing module may determine values for one or more other constantsof the polynomial (e.g., b=15 when the polynomial is of the formy=m×+b). Such a determination may be based on one or more of a lookup, arequest, a message, the message authentication key, and a command. Theprocessing module then solves for a constant of the polynomial (e.g.,constant m when the polynomial is of the form y=m×+b) to produce themessage authentication key based on the assigned value set of the firstvariable and the assigned value set of the second variable. Forinstance, y=m×+b, such that m=(y−b)/x=(175−15)/2=80=messageauthentication key.

Alternatively, the processing module decodes the received secret sharesin accordance with an error coding dispersal storage function torecapture the message authentication key when a threshold number of thesecret shares is received. For example, the processing module de-slicesthe received secret shares to produce de-sliced secret shares. Next, theprocessing module decodes the de-sliced secret shares to produce themessage authentication key.

The method continues at step 134 where the processing module assigns athreshold number of unique identifiers of a second threshold number ofreceived secret shares to the first variable of the polynomial toproduce a second assigned value set of the first variable. Next, theprocessing module assigns the second threshold number of received secretshares to the second variable of the polynomial to produce a secondassigned value set of the second variable. Next, the processing modulesolves for the constant of the polynomial to produce a second messageauthentication key based on the second assigned value set of the firstvariable and the second assigned value set of the second variable. In aninstance, y=m×+b, such that m=(y−b)/x=(255−15)/3=80=the second messageauthentication key, when the secret share is 255, the pillar is 3, andthe constant b=15.

Next, the processing module compares the second message authenticationkey with the message authentication key and indicates verification ofthe message authentication key when the comparing of the second messageauthentication key with the message authentication key is favorable(e.g. substantially the same). For example, the processing moduleindicates verification of the message authentication key when thecomparison of the second message authentication key=80 to the messageauthentication key=80 indicates a favorable comparison. The methodbranches to step 138 when the processing module determines that themessage authentication key is verified. The method branches to step 136when the processing module determines that the message authenticationkey is not verified. At step 136, the processing module discards encodeddata slices that corresponds (e.g., same pillar) to received secretshares that produced an invalid message authentication key. In addition,the processing module may send a delete command to the DSN memory todelete an encoded data slice associated with a secret share thatproduced the invalid message authentication key.

In another example, the processing module verifies the messageauthentication key based on received secret shares by testing more thanone combination of received secret shares to determine which pillars mayproduce the invalid message authentication key. In an instance, theprocessing module verifies the message authentication key to be verifiedwhen decoding of all combinations of the threshold number of secretshares result in the same message authentication key. In anotherinstance, the processing module determines the message authenticationkey to be not verified when the decoding of at least one of thethreshold number of secret shares result in a different messageauthentication key than the decoding of at least one other of thethreshold number of secret shares.

In yet another example, the processing module verifies the messageauthentication key based on comparing a received hash of thecryptographic key portion to a calculated hash of the cryptographic keyportion. The processing module determines that the messageauthentication key is verified when the comparison indicates that thereceived hash of the cryptographic key portion is substantially the sameas the calculated hash of the cryptographic key portion.

The method continues at step 138 where the processing module identifiesa received encoded data slice of the received encoded data slices havingan authentication code associated therewith when a threshold number ofthe encoded data slices is received. Next, the processing moduleperforms a keyed-hash message authentication code generation or acryptographic hash function algorithm on the received encoded data sliceutilizing the message authentication key to produce a verificationauthentication code.

The method continues at step 140 where the processing module comparesthe verification authentication code with the authentication code. Theprocessing module indicates verification of the authentication code whenthe comparing of the verification authentication code with theauthentication code is favorable (e.g., substantially the same).Alternatively, the processing module identifies a second receivedencoded data slice of the received encoded data slices having a secondauthentication code associated therewith when the threshold number ofthe encoded data slices is received. Next, the processing moduleverifies the second authentication code based on the messageauthentication key and the second received encoded data slice (e.g., theprocessing module performs the keyed-hash message authentication codegeneration or the cryptographic hash function algorithm on the secondreceived encoded data slice utilizing the message authentication key toproduce a second verification authentication code for comparison to thesecond authentication code). Next, the processing module indicatesverification of the authentication code when the first and secondauthentication codes are verified. The method branches to step 142 whenthe processing module determines that the received authenticationcode(s) are verified. The method branches to step 144 when theprocessing module determines that the received authentication code(s)are not verified.

At step 142, the processing module decodes the received encoded dataslices in accordance with an error coding dispersal storage function torecapture a data segment. At step 144, the processing module discardsencoded data slices associated with an authentication code that is notverified. In addition, the processing module may attempt to decode thereceived encoded data slices in accordance with the error codingdispersal storage function wherein the received encoded data slices areassociated with verified authorization codes to recapture the datasegment. In addition, the processing module may send a delete command tothe DS unit associated with the received encoded data slice associatedwith the authentication code that is not verified to delete the encodeddata slice associated with the authentication code that is not verified.

FIG. 9 is a flowchart illustrating an example of rebuilding encoded dataslices. The method begins at step 146 where a DS processing determines aDSN memory error including a missing data slice, a corrupted data slice,an offline DS unit, a network failure, etc. Such a determination may bebased on one or more of verification of slice name lists, validating astored slice checksum with a calculated slice checksum, a disk drivestatus, a memory status, an error message, and a command. Note that thememory error determination may be associated with a background processand/or upon an active data slice retrieval sequence.

At step 148, the DS processing determines a DS storage unit associatedwith the DSN memory error. The storage set comprises the DS unitsassigned as the storage locations for the n pillars of the vault. Such adetermination may be based on one or more of a vault lookup, a command,a predetermination, and the virtual DSN address to physical locationtable. At step 150, the DS processing determines DS unit metrics for theDS units of the associated DS storage set with the DSN memory error. TheDS unit metrics includes one or more of a ping time from the DSprocessing to the DS unit, throughput, uptime, security performance,reliability performance, and previous retrieval results. Such adetermination may be based on one or more of a vault lookup, a command,a predetermination, a history record, a previous measurement, and a realtime measurement.

The method continues at step 152 where the DS processing determines readDS units to facilitate a desired slice retrieval sequence. Such adetermination may be based on one or more of the DS unit metrics, analgorithm to choose the fastest response, a vault lookup, a command, apredetermination, a history record, a previous measurement, and a realtime measurement. For example, the DS processing chooses DS units ofpillars at the same site as the DS processing and in a second choice,chooses other DS units with the lowest ping times to facilitate fastretrieval.

At step 154, the DS processing retrieves EC data slices from the read DSunits by sending a retrieval command with slice names to the read DSunits and receiving retrieved slices. At step 156, the DS processingattempts to recreate the data object from the retrieved slices byde-slicing and decoding at least a read threshold k of the slices inaccordance with an error coded dispersal storage function. At step 158,the DS processing determines whether the data object recreation issuccessful based on a read threshold number of retrieved slices. Forexample, the DS processing determines an unsuccessful data objectrecreation when at least one data segment does not have at least a readthreshold number of retrieved slices to recreate the data segment. Themethod branches to step 162 when the DS processing determines that thedata object recreation is successful. The method continues to step 160when the DS processing determines that the data object recreation is notsuccessful. At step 160, the DS processing modifies the DS unit metricsto indicate a previous unsuccessful retrieval. The method branches backto step 152 where the DS processing determines the read DS units to tryagain.

The method continues at step 162 where the DS processing recreatesslices from the recreated data object in accordance with the error codeddispersal storage function. At step 164, the DS processing sends therecreated slices and slice names to the DS unit storage set with a storecommand to store the slices therein. In an example, the DS processingmay send the slices to the DS unit(s) where the DSN memory error wasdetected. In another example, the DS processing may send the slices tothe DS unit(s) where the DSN memory error was detected and at least oneother DS unit of the DS unit storage set. Note that the DS processingmay send the slices to the DS units one pillar at a time, all at once asa batch, or a combination thereof.

FIG. 10 is a flowchart illustrating another example of rebuildingencoded data slices. The method begins at step 166 where a DS processingdetermines a DSN memory error including a missing slice, a corruptedslice, an offline DS unit, a network failure, etc. Such a determinationmay be based on one or more of verification of slice name lists,validating a stored slice checksum with a calculated slice checksum, adisk drive status, a memory status, an error message, and a command.Note that the memory error determination may be associated with abackground process and/or upon an active slice retrieval sequence.

At step 168, the DS processing determines a DS storage unit associatedwith the DSN memory error. The storage set comprises the DS unitsassigned as the storage locations for the n pillars of the vault. Such adetermination may be based on one or more of a vault lookup, a command,a predetermination, and the virtual DSN address to physical locationtable. At step 170 The DS processing determines the DS unit pillar withthe DSN memory error based on one or more of a vault lookup, a command,a history record, a previous measurement, and a real time measurement.

At step 172, the DS processing retrieves EC data slices from one or moreof the DS units by sending a retrieval command with slice names to theread DS units and receiving retrieved slices. In an example, the DSprocessing sends the retrieval command(s) all at once to at least a readthreshold number of DS units of the DS storage set. Note that thesubsequent rebuilding may rebuild more than one pillar based onutilization of network bandwidth once to receive slices. In an instance,each rebuild for each pillar need not re-retrieve all the slices of thestorage set each time.

At step 174, the DS processing determines all of the DS unit pillarswith DSN memory error(s) based on one or more of, but not limited to theretrieved slices, a vault lookup, a command, a history record, aprevious measurement, and a real time measurement. For example, the DSprocessing determines that DS unit pillar 3 is in error when no slicewas received from DS unit pillar 3. At step 176, the DS processingrecreates the data object from the retrieved slices by de-slicing anddecoding at least a read threshold k of the slices in accordance with anerror coded dispersal storage function. At step 178, the DS processingrecreates slices from the recreated data object in accordance with theerror coded dispersal storage function.

The method continues with step 180 where the DS processing sends therecreated slices and slice names to the DS unit pillars with the DSmemory error(s) with a store command to store the slices therein. In anexample, the DS processing sends the slices to the DS unit(s) where theDSN memory error was detected. In another example, the DS processingsends the slices to the DS unit(s) where the DSN memory error wasdetected and at least one other DS unit of the DS unit storage set. Notethat the DS processing sends the slices to the DS units one pillar at atime or all at once.

FIG. 11 is a schematic block diagram of another embodiment of acomputing system. As illustrated, the system includes a plurality of DSunits 1-6 where DS units 1 and 2 are implemented at site 1, DS units 3and 4 are implemented at site 2, and DS units 5 and 6 are implemented atsite 3. As illustrated, DS unit 1 includes a storage integrityprocessing module 182 and a memory 184. In addition, DS units 2-6 mayinclude the storage integrity processing module 182 and the memory 184.The storage integrity processing module 182 includes functionality ofthe storage integrity processing unit enabling the DS unit to functionto rebuild EC data slices. The DS units 1-6 are operably coupled bylocal communications 186-190 (e.g., a local area network) when they areat the same site and by a network 24 (e.g., a wide area network) whenthey are not at the same site.

The storage integrity processing module 182 of the DS storage units 1-6is capable of reconstructing a data segment, based on receivingrecovered slices from at least some of the other DS storage units in acentralized fashion or each DS storage unit may sequentially compute aportion of the information to produce a reconstructed slice when theminimum number of good pillar slices has been included. In an example,the system has a pillar with n=6 and a read threshold k=4.

The DS units 1-6 communicate with each other to establish shared secretsby pairs of DS units (e.g., a shared secret between each combination oftwo DS units). The shared secret (e.g., common shared secret value) is anumber generated randomly by either of the DS units of the pair. Theshared secret number size may include any number of bytes. In an exampleof operation, DS unit 1 communicates with DS unit 5 to establish ashared secret S15 between them. Next, DS unit 5 generates a randomnumber F4A7 and sends the number to DS unit 1 as a proposed sharedsecret. Next, DS unit 1 accepts the proposal and sends a confirmationmessage to DS unit 5 that F4A7 is their shared secret.

Note that the shared secret may be encrypted such that a storedrepresentation of the shared secret is encrypted (e.g., with a publickey for the DS unit). The DS unit may decrypt the stored shared secretutilizing a private key associated with the DS unit. In addition, theshared secret may be encrypted such that a transmitted representation ofthe shared secret is encrypted (e.g., with a public key for thereceiving DS unit). The receiving DS unit may decrypt a received sharedsecret utilizing a private key associated with the receiving DS unit.

In an example, DS unit 1 and DS unit 5 establish shared secret S15(e.g., a unique shared secret value), DS unit 1 and DS unit 4 establishshared secret S14, DS unit 1 and DS unit 2 establish shared secret S12,DS unit 2 and DS unit 5 establish shared secret S25, DS unit 2 and DSunit 4 establish shared secret S24, and DS unit 4 and DS unit 5establish shared secret S45.

Any of the DS units 1-6 may detect a data slice error in memory and mayinitiate a rebuild sequence by sending a partial decode command (e.g.,partial rebuild requests) to at least a read threshold number (e.g.,decode threshold number) of other DS units of the storage set where thepartial decode command includes the pillar number of the detected error.The other DS units determine the partial (e.g., partial rebuilt slice),obfuscate the partial to create an obfuscated partial (e.g., securedpartial rebuilt slice), and send the obfuscated partial to the DS unitin response to receiving a partial decode command. The DS unitde-obfuscates each of the partials and recreates the data slice of thedata slice error and re-stores the slice. Note that none of the DS unitsreceive data slices from other DS units and the partials are sent overthe local communication or network in an obfuscated format to provideimproved security and confidentiality. The method is discussed ingreater detail with reference to FIGS. 12-16.

In another example of operation, a data slice error at DS unit 3 isdetected by the storage integrity processing module 182 of DS unit 3.The storage integrity processing module 182 identifies the slice namesto recover to reconstruct the slice in error based on the slice name ofthe failed slice. The storage integrity processing module 182 of DS unit3 sends a partial decode command to a read threshold number of the DSunits of the storage set (e.g., to DS units 1, 2, 4, 5). In an instance,the partial decode command includes an identity of the third pillar asthe pillar with the error and a list of the DS units that were sent thepartial decode command set (e.g., to DS units 1, 2, 4, 5).

In the example, DS unit 1 retrieves the requested data slice from itsmemory 184 and performs a partial decode step followed by a partialencode to produce a partial result P3,1 for the first slice pillar basedon knowing that it is the third pillar with the error. Note that thepartial decode and partial encode steps involve finite field arithmetic(e.g., a mathematical function) for the error control scheme and will bediscussed in greater detail with reference to FIGS. 13-16. Note that afundamental principle is that any slice can be recreated via combiningthe partial results from the companion data slices of companion pillars.

In the example, DS unit 1 retrieves the shared secrets S12, S14, and S15between DS unit 1 and the other DS units of the read threshold set(e.g., DS units 2, 4, 5). In the example, the DS unit 1 obfuscates thepartial result P3,1 utilizing an exclusive OR (XOR) logical functionwith each of the shared secrets S12, S14, S15 to produceP3,1⊕S12⊕S14⊕S15. The DS unit 1 sends the obfuscated partial to the DSunit 3. The method to create the obfuscated partial is discussed ingreater detail with reference to FIG. 13.

In the example, DS unit 2 retrieves the slice from its memory 184 andperforms a partial decode step followed by a partial encode step toproduce a partial result P3,2 for the second slice pillar based onknowing that it is the third pillar with the error. The DS unit 2retrieves the shared secrets S12, S24, and S25 between DS unit 2 and theother DS units of the read threshold set (e.g., DS units 1, 4, 5). Inthe example, DS unit 2 obfuscates the partial result P3,2 utilizing anexclusive OR (XOR) logical function with each of the shared secrets S12,S24, S25 to produce P3,2⊕S12⊕S24⊕S25. The DS unit 2 sends the obfuscatedpartial to the DS unit 3. The method to create the obfuscated partial isdiscussed in greater detail with reference to FIG. 14.

In the example, DS unit 4 retrieves the slice from its memory 184 andperforms a partial decode step followed by a partial encode step toproduce a partial result P3,4 for the fourth slice pillar based onknowing that it is the third pillar with the error. In the example, DSunit 4 retrieves the shared secrets S14, S24, and S45 between DS unit 4and the other DS units of the read threshold set (e.g., DS units 1, 2,5). Next, the DS unit 4 obfuscates the partial result P3,4 utilizing anexclusive OR (XOR) logical function with each of the shared secrets S14,S24, S45 to produce P3,4⊕S14⊕S24⊕S45. The DS unit 4 sends the obfuscatedpartial to the DS unit 3. The method to create the obfuscated partial isdiscussed in greater detail with reference to FIG. 15.

In the example, DS unit 5 retrieves the slice from its memory 184 andperforms a partial decode step followed by a partial encode step toproduce a partial result P3,5 for the fifth slice pillar based onknowing that it is the third pillar with the error. Next, DS unit 5retrieves the shared secrets S15, S25, and S45 between DS unit 5 and theother DS units of the read threshold set (e.g., DS units 1, 2, 4). Inexample, DS unit 5 obfuscates the partial result P3,5 utilizing anexclusive OR (XOR) logical function with each of the shared secrets S15,S25, S45 to produce P3,5⊕S15⊕S25⊕S45. The DS unit 5 sends the obfuscatedpartial to the DS unit 3. The method to create the obfuscated partial isdiscussed in greater detail with reference to FIG. 16.

In the example, DS unit 3 receives the obfuscated partials from DS units1, 2, 4, and 5. The DS unit 3 utilizes an obfuscation decoder to producethe desired pillar three slice based on the received obfuscatedpartials. In an instance, the obfuscation decoder XORs the obfuscatedpartials with each other to produce the desired slice. Note that the XORof the four obfuscated partials will cancel out the twelve sharedsecrets since there are two identical shared secrets (e.g., one pair) ofthe six permutations of DS unit pairs amongst the four DS units. In aninstance, the recreated slice of pillar three can be written as:

 = P 3, 1 ⊕ S 12 ⊕ S 14 ⊕ S 15 ⊕ P 3, 2 ⊕ S 12 ⊕ S 24 ⊕ S 25 ⊕ P 3, 4 ⊕ S 14 ⊕ S 24 ⊕ S 45 ⊕ P 3, 5 ⊕ S 15 ⊕ S 25 ⊕ S 45 = P 3, 1 ⊕ P 3, 2 ⊕ P 3, 4 ⊕ P 3, 5 ⊕ S 12 ⊕ S 12 ⊕ S 14 ⊕ S 14 ⊕ S 15 ⊕ S 15 ⊕ S 24 ⊕ S 24 ⊕ S 25 ⊕ S 25 ⊕ S 45 ⊕ S 45 = P 3, 1 ⊕ P 3, 2 ⊕ P 3, 4 ⊕ P 3, 5 = Slice  3

Next, DS unit 3 stores the re-created pillar three slice in memory 184thus completing rebuilding sequence to correct the slice failure.

FIG. 12 is a schematic block diagram of an embodiment of a plurality ofgrid modules 82. As illustrated, grid module 82 includes a post-slicede-manipulator 81, a de-slicer 87, a partial decoder 192, a partialencoder 194, an obfuscation encoder 196, and an obfuscation decoder 197.A single grid module 82 may perform tasks on every pillar (e.g., all theDS units for this storage set) or the grid module 82 may perform thetasks on one pillar. In an example, the post-slice de-manipulator 81performs a de-manipulation (e.g., CRC) on the good EC data slice beforesending the slice to the de-slicer 87. The de-slicer 87 de-slices theslice to create its portion of the encoded data segment. In an instance,the de-slicer may be null. The partial decoder for 92 performs a decodefunction on the portion of the encoded data segment to produce apartially decoded portion of the data segment. The partial encoder 194encodes the partially decoded portion of the data segment to produce apartially encoded portion of the data segment for this pillar.

In an example, slice 1_2 is in error at site 2. The grid module 82processes slices 1_0, 1_1, 1_3, and 1_4, to create correspondingpartials P3,1, P3,2, P3,4, and P3,5 which are obfuscated by an XOR witheach of the stored shared secrets for the pillars in the retrievalsequence as discussed previously. The grid module 82 at site 2 mayutilize an obfuscation decoder 197 on the obfuscated partials withfinite field arithmetic to produce and locally store the desiredreconstructed slice 1_2. The arithmetic will be discussed in greaterdetail with reference to FIGS. 13-16.

FIG. 13 is a schematic block diagram of another embodiment of a gridmodule. As illustrated, the grid module includes a partial decoder 192,a partial encoder 194, and an obfuscation encoder 196. Together, theytransform a known good slice into an obfuscated partial result that islater combined with other such partial results to determine a particularmissing slice from the same data segment. In an example, FIGS. 13-16illustrate the sequential steps to reconstruct a failed pillar 3 slice.In the example, the error control approach utilizes six pillars andrequires at least four good pillars to reconstruct a missing slice(e.g., a 6/4 system). The example will illustrate utilizing pillars 1,2, 4, and 5 to reconstruct the missing pillar 3 slice. The high levelapproach starts with each of the four encoder/decoder pairs creatingtheir obfuscated partial result.

The partial decoder 192 matrix multiples an incoming good pillar 1 sliceS1 from DS unit 1 at site 1 times a matrix A′ (e.g., a reduced matrix)where the number of rows equals the number of pillars and the number ofcolumns equals the minimum number of required pillars for decoding. Thefirst column is populated with random numbers a, b, d, e, and f. In aninstance, these numbers are be different for in the matrix A′ of theother pillars. Note that there is no need for a number c in the thirdrow since that is the missing pillar row, nor the last row (f) sinceonly four of the six pillars are required for reconstruction. The resultis a vector d=aS1, bS1, dS1, e S1.

The partial encoder 194 matrix multiples the vector d times a matrix A(e.g., encoding matrix) where the number of rows equals the number ofpillars and the number of columns equals the minimum number of requiredpillars for decoding. All the rows are blanked out of the encodingmatrix except for row 3 which is populated with entries 9, 10, 11, 12representing the entry numbers of the A matrix. These same numbers willbe used in all the other partial encoders for the other pillars. Thepartial encoder produces the partial result for missing pillar 3, goodpillar 1 as P3,1=9 aS1+10 bS1+11 dS1+12 eS1.

The obfuscation encoder 196 performs the XOR function of P3,1 with eachof the shared secrets S12, S14, ad S15 to produce the obfuscated partialP3,1⊕S12⊕S14⊕S15 for slice 1. The grid module 82 sends the obfuscatedpartial to DS unit 3.

FIG. 14 is a schematic block diagram of another embodiment of a gridmodule. As illustrated, the grid module includes a partial decoder 192,a partial encoder 194, and an obfuscation encoder 196. Together, theytransform a known good slice into an obfuscated partial result that islater combined with other such partial results to determine a particularmissing slice from the same data segment.

In the continuing example, DS unit 2 partial decoder 192 matrixmultiples the incoming good pillar 2 slice S2 from DS unit 2 at site 1times a matrix A′ where the number of rows equals the number of pillarsand the number of columns equals the minimum number of required pillarsfor decoding. In an instance, the second column is populated with randomnumbers g, h, j, k, and l. Note that these numbers are different for inthe matrix A′ of the other pillars. Note that there is no need for anumber i in the third row since that is the missing pillar row, nor thelast row (l) since only four of the six pillars are required forreconstruction. The result is a vector d=gS2, hS2, jS2, kS2.

The partial encoder 194 matrix multiples the vector d times a matrix Awhere the number of rows equals the number of pillars and the number ofcolumns equals the minimum number of required pillars for decoding. Notethat the rows are blanked out except for row 3 which is populated withentries 9, 10, 11, 12 representing the entry numbers of the A matrix.These same numbers will be used in all the other partial encoders forthe other pillars. The partial encoder 184 produces the partial resultfor missing pillar 3, good pillar 2 as P3,2=9 gS2+10 hS2+11 jS2+12 kS2.

The obfuscation encoder 196 performs the XOR function of P3,2 with eachof the shared secrets S12, S24, ad S25 to produce the obfuscated partialP3,2⊕S12⊕S24⊕S25 for slice 2. The grid module 82 sends the obfuscatedpartial to DS unit 3.

FIG. 15 is a schematic block diagram of another embodiment of a gridmodule. As illustrated, the grid module includes a partial decoder 192,a partial encoder 194, and an obfuscation encoder 196. Together, theytransform a known good slice into an obfuscated partial result that islater combined with other such partial results to determine a particularmissing slice from the same data segment.

The example continues where DS unit 4 partial decoder 192 matrixmultiples the incoming good pillar 4 slice S4 from DS unit 4 at site 2times a matrix A′ where the number of rows equals the number of pillarsand the number of columns equals the minimum number of required pillarsfor decoding. Note that the third column is populated with randomnumbers m, n, p, q, and r. An instance, these numbers will be differentfor in the matrix A′ of the other pillars. Note that there is no needfor a number o in the third row since that is the missing pillar row,nor the last row (r) since only four of the six pillars are required forreconstruction. The result is a vector d=mS4, nS4, pS4, qS4.

The partial encoder 194 matrix multiples the vector d times a matrix Awhere the number of rows equals the number of pillars and the number ofcolumns equals the minimum number of required pillars for decoding. Allthe rows are blanked out except for row 3 which is populated withentries 9, 10, 11, 12 representing the entry numbers of the A matrix.Note that these same numbers will be used in all the other partialencoders for the other pillars. The partial encoder produces the partialresult for missing pillar 3, good pillar 4 as P3,4=9 mS4+10 nS4+11pS4+12 qS4.

The obfuscation encoder 196 performs the XOR function of P3,4 with eachof the shared secrets S14, S24, ad S45 to produce the obfuscated partialP3,4⊕S14⊕S24⊕S45 for slice 4. The grid module 82 sends the obfuscatedpartial to DS unit 3.

FIG. 16 is a schematic block diagram of another embodiment of a gridmodule. As illustrated, the grid module 82 a partial decoder 192, apartial encoder 194, and an obfuscation encoder 196. Together, theytransform a known good slice into an obfuscated partial result that islater combined with other such partial results to determine a particularmissing slice from the same data segment.

In the continuing example, DS unit 5 partial decoder 192 matrixmultiples the incoming good pillar 5 slice S5 from DS unit 5 at site 3times a matrix A′ where the number of rows equals the number of pillarsand the number of columns equals the minimum number of required pillarsfor decoding. Note that the fourth column is populated with randomnumbers s, t, v, w, and x. In an instance, these numbers will bedifferent for in the matrix A′ of the other pillars. Note that there isno need for a number u in the third row since that is the missing pillarrow, nor the last row (x) since only four of the six pillars arerequired for reconstruction. The result is a vector d=sS5, tS5, vS5,wS5.

Next, partial encoder 194 matrix multiples the vector d times a matrix Awhere the number of rows equals the number of pillars and the number ofcolumns equals the minimum number of required pillars for decoding. Notethat all the rows are blanked out except for row 3 which is populatedwith entries 9, 10, 11, 12 representing the entry numbers of the Amatrix. In an instance, these same numbers will be used in all the otherpartial encoders for the other pillars. Next, partial encoder 94produces the partial result for missing pillar 3, good pillar 5 asP3,5=9 sS5+10 tS5+11 vS5+12 wS5.

The obfuscation encoder 196 performs the XOR function of P3,5 with eachof the shared secrets S15, S25, ad S45 to produce the obfuscated partialP3,5⊕S15⊕S25⊕S45 for slice 5. The grid module 82 sends the obfuscatedpartial to DS unit 3 where the obfuscation decoder 197 decodes the forpartials to produce the re-created slice 1_2. Next, DS unit 3 stores there-created slice in the memory 184 to substantially complete therebuilding process described in example.

FIG. 17 is a flowchart illustrating an example of optimizing memoryusage by a storage integrity processing unit. The method begins withstep 198 where a DS processing of (e.g., one of the storage integrityprocessing unit, the DS managing unit, the DS processing unit, the DSunit, and/or the user device) determines a slice to investigate fordeletion. Such a determination may be based on one or more of a randomslice on a DS unit, a random slice in the computing system, a firstslice of the first vault, a last slice that was investigated, an errormessage, an error detection, a priority indicator, a security indicator,a predetermination, and a command.

At step 200, the DS processing determines DS storage units of a DSstorage set associated with the slice. Note that the storage setcomprises DS units assigned as the storage locations for the n pillarsof a vault. Such a determination may be based on one or more of a vaultlookup, a command, a predetermination, and the virtual DSN address tophysical location table. At step 202, the DS processing retrieves ECdata slices from all n (e.g., pillar width) of the DS units by sending aretrieval command to the DS units of additional storage set and byreceiving retrieved slices.

The method continues with step 204 where the DS processing determines ifslices for all n pillars were received by counting them and/or matchingslice names to pillar numbers. The method branches back to step 198(e.g., to go to the next slice) when the DS processing determines thatslices for all n pillars were received. The method continues to step 206when the DS processing determines that the slices for all n pillars werenot received. At step 206, the DS processing attempts to recreate thedata segment from the retrieved slices decoding at least a readthreshold k of the slices in accordance with an error coding dispersalstorage function. Next, the DS processing determines if the data segmentrecreation was successful based on a read threshold number of retrievedslices. For example, the DS processing determines an unsuccessful dataobject recreation when at least one data segment does not have at leasta read threshold number of retrieved slices to recreate the datasegment. The method branches to step two into when the DS processingdetermines that the data segment recreation was successful. The methodcontinues to step 210 one the DS processing determines that the datasegment recreation was not successful. At step 210, the DS processingsends a delete command to the DS units for this data segment to deleteall the slice names associated with the data segment. Note that themethod provides an improvement to free up memory when partial dataexists that is not recoverable.

At step 212, the DS processing recreates slices from the recreated datasegments of the data object in accordance with the error codingdispersal storage function when the DS processing determines that allthe data segments were successfully recreated for the data object. Atstep 214, the DS processing sends the recreated slices and slice namesto the DS unit storage set with a store command to store the slices. Inan example, the DS processing sends the slices to the DS unit(s) wherethe slices were missing. In another example, the DS processing sends theslices to the DS unit(s) where the slices were missing and at least oneother DS unit of the DS unit storage set. In an instance, the DSprocessing sends the slices to the DS units one pillar at a time. Inanother instance, the DS processing sends the slices to the DS units allat once as a batch.

FIG. 18 is a flowchart illustrating another example of optimizing memoryusage. The method begins at step 216 where a DS processing (e.g., of oneof the storage integrity processing unit, the DS managing unit, the DSprocessing unit, the DS unit, and/or the user device) determines a dataobject to investigate for deletion. Such a determination may be based onone or more of a random data object on a DS unit, a data object in thecomputing system, a first data object of the first vault, a last dataobject that was investigated, an error message, an error detection, apriority indicator, a security indicator, a predetermination, and acommand.

At step 218, the DS processing determines a number of data segments thatshould exist based on vault information for data object. At step 220,the DS processing determines DS storage units of the DS storage setassociated with the data object. Such a determination may be based onone or more of a vault lookup, a command, a predetermination, and avirtual DSN address to physical location table lookup. At step 222, theDS processing retrieves at least one EC data slices from at least one ofthe n pillars of the DS units by sending a retrieval command to the DSunits and by receiving retrieved slices.

The method continues at step 224 where the DS processing determineswhether at least one EC data slice from at least one of the n pillars ofthe DS units for each data segment were received by counting them and/ormatching slice names, data segment IDs, to pillar numbers. Note that thesegments may all be present when at least one slice is retrieved fromeach data segment of the data object. The method branches back to step216 (e.g., to go to the next data object) when the DS processingdetermines that at least one slices for all the data segments werereceived. The method continues to step 226 when the DS processingdetermines that least one EC data slice from at least one of the npillars of the DS units for each data segment were not received.

At step 226, the DS processing determines a disposition method ofmissing data segments. Such a method includes deleting the data objector filling missing data segment(s). Such a determination may be based onone or more of a vault lookup, a command, a predetermination, a priorityindicator, a security indicator, and a data object type. For example,the DS processing determines to delete the data object when the datatype is a software program backup that cannot tolerate errors. Inanother example, the DS processing determines to fill the missing datasegment(s) of a data object when the data type is a video file that cantolerate errors. The method branches to step 230 when the DS processingdetermines the disposition method of missing data segments to be to fillthe segments. The method continues to step 228 when the DS processingdetermines the disposition method of missing data segments to be todelete the data object. At step 228, the DS processing sends a deleteslice command to the DS units that have slice names associated with thedata object. In an instance, the DS processing deletes the data objectname from an associated vault. In another instance, DS processingdeletes a directory reference of the data object name from a directory.

At step 230, the DS processing determines filler data segment(s) formissing segment(s) of the data object. For example, the filler mayinclude all zeroes, all ones, a pattern, a predetermined number, areceived number, a backup data segment, or a hash of data (e.g., thedata segment ID, the data object ID, the remaining data object, etc.).Such a determination may be based on one or more of a vault lookup, acommand, a predetermination, a security indicator, a priority indicator,and a data type. At step 232, the DS processing creates slices from thefiller data segment(s) of the data object in accordance with an errorcoding dispersal storage function. At step 234, the DS processing sendsthe slices and associated slice names to the DS unit storage set with astore command to store the slices there in. In an example, the DSprocessing sends the slices to the DS unit(s) where the data segment(s)were missing. In another example, the DS processing sends the slices tothe DS unit(s) where the data segment(s) were missing and at least oneother DS unit of the DS unit storage set. In an instance, the DSprocessing sends the slices to the DS units one pillar at a time. Inanother instance, the DS processing sends the slices to the DS units allat once.

FIG. 19 is a flowchart illustrating another example of optimizing memoryusage. The method begins with step 236 where a DS processing (e.g., ofone of the storage integrity processing unit, the DS managing unit, theDS processing unit, the DS unit, and/or the user device) determines adata object name in the directory to investigate for deletion. Such adetermination may be based on one or more of a random data object on aDS unit, a data object in the computing system, a first data object ofthe first vault, a last data object that was investigated, an errormessage, an error detection, a priority indicator, a security indicator,a predetermination, and a command.

At step 238, the DS processing determines the data segments that shouldexist based on vault information for data object. At step 240, the DSprocessing determines DS storage units of the DS storage set associatedwith the data object. Such a determination may be based on one or moreof a vault lookup, a command, a predetermination, and the virtual DSNaddress to physical location table. At step 242, the DS processingretrieves at least one EC data slice from at least one data segment ofthe data object from the DS units by sending a retrieval command withslice names to the DS units and by receiving retrieved slices.

The method continues at step 244 where the DS processing determineswhether at least one EC data slice from at least one of the n pillars ofthe DS units of at least one data segment of the data object wasreceived by counting them and/or matching slice names, data segment IDs,to pillar numbers. Note that the data object may be present when atleast one slice is retrieved from at least one data segment of the dataobject. The method branches back to step 236 where the DS processingdetermines the data object in the directory (e.g., to go to the nextdata object) when the DS processing determines that at least one sliceexists for the data. The method continues to step 246 when the DSprocessing determines that least one EC data slice of the data objectwas not received. At step 246, the DS processing deletes the data objectname from the directory since no slices exist for the data object.

FIG. 20 is a flowchart illustrating another example of optimizing memoryusage. The method begins at step 248 where a DS processing (e.g., of oneof the storage integrity processing unit, the DS managing unit, the DSprocessing unit, the DS unit, and/or the user device) determines a slicename in DSN memory to investigate for deletion. Such a determination maybe based on one or more of a random slice on a DS unit, a random slicein the computing system, the first slice of the first vault, the lastslice that was investigated, an error message, an error detection, apriority indicator, a security indicator, a predetermination, and acommand.

The method continues at step 250 where the DS processing determines if adata object name exists in a directory linked to the slice name. Such adetermination may be based on converting the slice name into a sourcename and checking the directory vault for the source name. The DSprocessing determines the slice name is linked to a data object name inthe directory when the source name is found. The method branches back tostep 248 (e.g., to go to another slice name) when the DS processingdetermines that the data object name exists in a directory linked to theslice name. The method continues to step 252 when the DS processingdetermines that the data object name does not exist in a directorylinked to the slice name. At step 252, the DS processing unit links theslice name to a lost and found directory for potential subsequentprocessing. Additionally, the DS processing may delete the slice (e.g.,a lost slice). In another instance, the DS processing links the slice toa different data object name in the directory (e.g., a found slice).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “operably coupled to”, “coupled to”, and/or “coupling” includesdirect coupling between items and/or indirect coupling between items viaan intervening item (e.g., an item includes, but is not limited to, acomponent, an element, a circuit, and/or a module) where, for indirectcoupling, the intervening item does not modify the information of asignal but may adjust its current level, voltage level, and/or powerlevel. As may further be used herein, inferred coupling (i.e., where oneelement is coupled to another element by inference) includes direct andindirect coupling between two items in the same manner as “coupled to”.As may even further be used herein, the term “operable to” or “operablycoupled to” indicates that an item includes one or more of powerconnections, input(s), output(s), etc., to perform, when activated, oneor more its corresponding functions and may further include inferredcoupling to one or more other items. As may still further be usedherein, the term “associated with”, includes direct and/or indirectcoupling of separate items and/or one item being embedded within anotheritem. As may be used herein, the term “compares favorably”, indicatesthat a comparison between two or more items, signals, etc., provides adesired relationship. For example, when the desired relationship is thatsignal 1 has a greater magnitude than signal 2, a favorable comparisonmay be achieved when the magnitude of signal 1 is greater than that ofsignal 2 or when the magnitude of signal 2 is less than that of signal1.

While the transistors in the above described figure(s) is/are shown asfield effect transistors (FETs), as one of ordinary skill in the artwill appreciate, the transistors may be implemented using any type oftransistor structure including, but not limited to, bipolar, metal oxidesemiconductor field effect transistors (MOSFET), N-well transistors,P-well transistors, enhancement mode, depletion mode, and zero voltagethreshold (VT) transistors.

The present invention has also been described above with the aid ofmethod steps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claimed invention.

The present invention has been described, at least in part, in terms ofone or more embodiments. An embodiment of the present invention is usedherein to illustrate the present invention, an aspect thereof, a featurethereof, a concept thereof, and/or an example thereof. A physicalembodiment of an apparatus, an article of manufacture, a machine, and/orof a process that embodies the present invention may include one or moreof the aspects, features, concepts, examples, etc. described withreference to one or more of the embodiments discussed herein.

The present invention has been described above with the aid offunctional building blocks illustrating the performance of certainsignificant functions. The boundaries of these functional buildingblocks have been arbitrarily defined for convenience of description.Alternate boundaries could be defined as long as the certain significantfunctions are appropriately performed. Similarly, flow diagram blocksmay also have been arbitrarily defined herein to illustrate certainsignificant functionality. To the extent used, the flow diagram blockboundaries and sequence could have been defined otherwise and stillperform the certain significant functionality. Such alternatedefinitions of both functional building blocks and flow diagram blocksand sequences are thus within the scope and spirit of the claimedinvention. One of average skill in the art will also recognize that thefunctional building blocks, and other illustrative blocks, modules andcomponents herein, can be implemented as illustrated or by discretecomponents, application specific integrated circuits, processorsexecuting appropriate software and the like or any combination thereof.

What is claimed is:
 1. A method comprises: determining, by a computingdevice of a dispersed storage network (DSN), that an encoded data sliceof a set of encoded data slices requires rebuilding, wherein a datasegment is encoded using an error coding dispersal storage function toproduce the set of encoded data slices; sending, by the computingdevice, partial rebuild requests to storage units of the DSN, whereinthe set of encoded data slices are distributedly stored within thestorage units, wherein the partial rebuild requests include identity ofthe storage units and are generated by the computing device; generating,by one of the storage units, a partial rebuilt slice based one or moreencoded data slices of the set of encoded data slices stored by the oneof the storage units; securing, by the one of the storage units, thepartial rebuilt slice using a shared secret scheme that is shared amongthe storage units to produce a secured partial rebuilt slice, whereinthe shared secret scheme includes using shared secret values of otherstorage units to produce the secured partial rebuilt slice; receiving,by the computing device, a set of secured partial rebuilt slices fromthe storage units; recovering, by the computing device, a set of partialrebuilt slices from the set of secured partial rebuilt slices based onthe shared secret values of the storage units; and rebuilding, by thecomputing device, the encoded data slice from the set of partial rebuiltslices.
 2. The method of claim 1, wherein the computing devicedetermining that the encoded data slice requires rebuilding furthercomprises: identifying the storage units from a plurality of storageunits of the DSN for facilitating the rebuilding.
 3. The method of claim1, wherein the one of the storage units generating the partial rebuiltslice comprises: obtaining an encoding matrix associated with the errorcoding dispersal storage function; reducing the encoding matrix forgenerating the partial rebuilt slice to produce a reduced matrix;inverting the reduced matrix to produce a partial decode matrix; andgenerating the partial rebuilt slice based on the partial decode matrixand the one or more encoded data slices of the set of encoded dataslices stored by the one of the storage units.
 4. The method of claim 1,wherein the one of the storage units generating the partial rebuiltslice comprises: providing a copy of the one or more encoded data slicesas the partial rebuilt slice.
 5. The method of claim 1, wherein the oneof the storage units securing the partial rebuilt slice furthercomprises: identifying a common shared secret value that is commonlyshared by the storage units and the computing device; and securing thepartial rebuilt slice using the common shared secret value.
 6. Themethod of claim 1, wherein the one of the storage units securing thepartial rebuilt slice further comprises: identifying a unique sharedsecret value that is commonly shared by the one of the storage units andthe computing device; and securing the partial rebuilt slice using theunique shared secret value.
 7. The method of claim 1, wherein therecovering the set of partial rebuilt slices comprises: exclusive ORingthe set of secure partial rebuilt slices with the shared secret valuesof the storage units, wherein the computing device received 2*n copiesof each of the shared secret values, and wherein n is greater than orequal to one.
 8. The method of claim 1, wherein the computing devicerecovers the set of partial rebuilt slices further comprises at leastone of: decrypting the set of secured partial rebuilt slices using acommon shared secret value, wherein the common shared secret value thatis commonly shared by the storage units and the computing device; andperforming a mathematical function on the set of secured partial rebuiltslices using the common shared secret value.
 9. A dispersed storagenetwork (DSN) comprises: a computing device that includes a processingmodule and memory; and storage units, wherein each storage unit of thestorage units includes a storage processing module and storage memory,wherein: the processing module determines that an encoded data slice ofa set of encoded data slices requires rebuilding, wherein a data segmentis encoded using an error coding dispersal storage function to producethe set of encoded data slices; the processing module sends partialrebuild requests to the storage units, wherein the set of encoded dataslices are distributedly stored within the storage units, herein thepartial rebuild requests include identity of the storage units and aregenerated by the processing module; a storage processing module of oneof the storage units generates a partial rebuilt slice based on one ormore encoded data slices of the set of encoded data slices stored in astorage memory of the one of the storage units; the storage processingmodule of the one of the storage units secures the partial rebuilt sliceusing a shared secret scheme that is shared among the storage units toproduce a secured partial rebuilt slice, wherein the shared secretscheme includes using shared secret values of other storage units toproduce the secured partial rebuilt slice; the processing modulereceives a set of secured partial rebuilt slices from the storage units;the processing module recovers a set of partial rebuilt slices from theset of secured partial rebuilt slices based on the shared secret valuesof the storage units; and the processing module rebuilds the encodeddata slice from the set of partial rebuilt slices.
 10. The DSN of claim9, wherein the processing module further determines that the encodeddata slice requires rebuilding by: identifying the storage units from aplurality of storage units of the DSN for facilitating the rebuilding.11. The DSN of claim 9, wherein the storage processing module of the oneof the storage units generates the partial rebuilt slice by: obtainingan encoding matrix associated with the error coding dispersal storagefunction; reducing the encoding matrix for generating the partialrebuilt slice to produce a reduced matrix; inverting the reduced matrixto produce a partial decode matrix; and generating the partial rebuiltslice based on the partial decode matrix and the one or more encodeddata slices of the set of encoded data slices stored in the storagememory of the one of the storage units.
 12. The DSN of claim 9, whereinthe storage processing module of the one of the storage units generatesthe partial rebuilt slice by: providing a copy of the one or moreencoded data slices, from the storage memory of the one of the storageunits, as the partial rebuilt slice.
 13. The DSN of claim 9, wherein thestorage processing module of the one of the storage units furthersecures the partial rebuilt slice by: identifying a common shared secretvalue that is commonly shared by the storage units and the computingdevice; and securing the partial rebuilt slice using the common sharedsecret value.
 14. The DSN of claim 9, wherein the storage processingmodule of the one of the storage units further secures the partialrebuilt slice by: identifying a unique shared secret value that iscommonly shared by the one of the storage units and the computingdevice; and securing the partial rebuilt slice using the unique sharedsecret value.
 15. The DSN of claim 9, wherein the processing modulerecovers the set of partial rebuilt slices by: exclusive ORing the setof secured partial rebuilt slices with the shared secret values of thestorage units, wherein the computing device received 2*n copies of eachof the shared secret values, and wherein n is greater than or equal toone.
 16. The DSN of claim 9, wherein the processing module furtherrecovers the set of partial rebuilt slices by at least one of:decrypting the set of secured partial rebuilt slices using a commonshared secret value, wherein the common shared secret value that iscommonly shared by the storage units and the computing device; andperforming a mathematical function on the set of secured partial rebuiltslices using the common shared secret value.